Six supervised learning classifiers were used: SVM, RF, gradient boost decision tree (GBDT) for classification, XGBoost, and feed-forward neural network with several sets of features (statistics, POS counts, LIWC features, TF-IDF vectors, and topic probability features) and found that combining more features increases the performance of all methods. RF gained better performance than most models except for the metric of Precision, in which the neural network model achieves slightly better results [1]}. Different classifiers were also examined by X. Huang et al., including SVM, NB, LR, J48 classifier, RF, and Sequential minimal optimization (SMO) with three N-gram features. SVM classifier achieves the best performance in comparison with other classifiers, with an F1-score of \(68.3\%\) , a Precision of \(78.9\%\) , a recall of \(60.3\%\) , and accuracy over \(94.0\%\)   [2]}.
